Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations45466
Missing cells506
Missing cells (%)0.1%
Duplicate rows27
Duplicate rows (%)0.1%
Total size in memory5.9 MiB
Average record size in memory136.0 B

Variable types

Categorical2
Text9
Unsupported1
Numeric4
Boolean1

Alerts

Dataset has 27 (0.1%) duplicate rowsDuplicates
revenue is highly overall correlated with vote_countHigh correlation
vote_count is highly overall correlated with revenueHigh correlation
adult is highly imbalanced (99.8%) Imbalance
status is highly imbalanced (97.0%) Imbalance
video is highly imbalanced (97.9%) Imbalance
popularity is an unsupported type, check if it needs cleaning or further analysis Unsupported
revenue has 38052 (83.7%) zeros Zeros
runtime has 1558 (3.4%) zeros Zeros
vote_average has 2998 (6.6%) zeros Zeros
vote_count has 2899 (6.4%) zeros Zeros

Reproduction

Analysis started2025-03-05 09:18:47.426243
Analysis finished2025-03-05 09:18:50.482955
Duration3.06 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

adult
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
False
45454 
True
 
9
- Written by Ørnås
 
1
Rune Balot goes to a casino connected to the October corporation to try to wrap up her case once and for all.
 
1
Avalanche Sharks tells the story of a bikini contest that turns into a horrifying affair when it is hit by a shark avalanche.
 
1

Length

Max length126
Median length5
Mean length5.0050807
Min length4

Characters and Unicode

Total characters227561
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowFalse
2nd rowFalse
3rd rowFalse
4th rowFalse
5th rowFalse

Common Values

ValueCountFrequency (%)
False 45454
> 99.9%
True 9
 
< 0.1%
- Written by Ørnås 1
 
< 0.1%
Rune Balot goes to a casino connected to the October corporation to try to wrap up her case once and for all. 1
 
< 0.1%
Avalanche Sharks tells the story of a bikini contest that turns into a horrifying affair when it is hit by a shark avalanche. 1
 
< 0.1%

Length

2025-03-05T11:18:50.609287image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T11:18:50.657865image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
false 45454
99.9%
true 9
 
< 0.1%
to 4
 
< 0.1%
a 4
 
< 0.1%
the 2
 
< 0.1%
avalanche 2
 
< 0.1%
by 2
 
< 0.1%
when 1
 
< 0.1%
contest 1
 
< 0.1%
hit 1
 
< 0.1%
Other values (32) 32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 45479
20.0%
a 45475
20.0%
s 45465
20.0%
l 45461
20.0%
F 45454
20.0%
49
 
< 0.1%
r 25
 
< 0.1%
t 23
 
< 0.1%
o 19
 
< 0.1%
n 17
 
< 0.1%
Other values (24) 94
 
< 0.1%

budget
Text

Distinct1226
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:50.792553image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length32
Median length1
Mean length2.2153917
Min length1

Characters and Unicode

Total characters100725
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839 ?
Unique (%)1.8%

Sample

1st row30000000
2nd row65000000
3rd row0
4th row16000000
5th row0
ValueCountFrequency (%)
0 36573
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1216) 6821
 
15.0%
2025-03-05T11:18:50.973331image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 84525
83.9%
1 3222
 
3.2%
5 3201
 
3.2%
2 2555
 
2.5%
3 1792
 
1.8%
4 1325
 
1.3%
6 1147
 
1.1%
7 1119
 
1.1%
8 1102
 
1.1%
9 660
 
0.7%
Other values (39) 77
 
0.1%

genres
Text

Distinct4069
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:51.059606image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length264
Median length225
Mean length62.822131
Min length2

Characters and Unicode

Total characters2856271
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2365 ?
Unique (%)5.2%

Sample

1st row[{'id': 16, 'name': 'Animation'}, {'id': 35, 'name': 'Comedy'}, {'id': 10751, 'name': 'Family'}]
2nd row[{'id': 12, 'name': 'Adventure'}, {'id': 14, 'name': 'Fantasy'}, {'id': 10751, 'name': 'Family'}]
3rd row[{'id': 10749, 'name': 'Romance'}, {'id': 35, 'name': 'Comedy'}]
4th row[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]
5th row[{'id': 35, 'name': 'Comedy'}]
ValueCountFrequency (%)
id 91106
24.6%
name 91106
24.6%
drama 20265
 
5.5%
18 20265
 
5.5%
35 13182
 
3.6%
comedy 13182
 
3.6%
53 7624
 
2.1%
thriller 7624
 
2.1%
romance 6735
 
1.8%
10749 6735
 
1.8%
Other values (71) 92873
25.1%
2025-03-05T11:18:51.225308image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2856271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2856271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2856271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 546636
19.1%
325231
 
11.4%
: 182212
 
6.4%
a 152966
 
5.4%
e 146936
 
5.1%
m 144238
 
5.0%
, 139188
 
4.9%
i 130819
 
4.6%
n 126822
 
4.4%
d 107792
 
3.8%
Other values (46) 853431
29.9%

id
Text

Distinct45436
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:51.398188image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.2514846
Min length1

Characters and Unicode

Total characters238764
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45407 ?
Unique (%)99.9%

Sample

1st row862
2nd row8844
3rd row15602
4th row31357
5th row11862
ValueCountFrequency (%)
141971 3
 
< 0.1%
159849 2
 
< 0.1%
168538 2
 
< 0.1%
298721 2
 
< 0.1%
265189 2
 
< 0.1%
5511 2
 
< 0.1%
97995 2
 
< 0.1%
99080 2
 
< 0.1%
23305 2
 
< 0.1%
119916 2
 
< 0.1%
Other values (45426) 45445
> 99.9%
2025-03-05T11:18:51.628021image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32923
13.8%
2 28625
12.0%
3 26732
11.2%
4 24747
10.4%
5 21996
9.2%
6 21184
8.9%
7 20949
8.8%
8 20909
8.8%
9 20485
8.6%
0 20208
8.5%
Distinct45417
Distinct (%)99.9%
Missing17
Missing (%)< 0.1%
Memory size355.3 KiB
2025-03-05T11:18:51.784690image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.9994719
Min length1

Characters and Unicode

Total characters409017
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45387 ?
Unique (%)99.9%

Sample

1st rowtt0114709
2nd rowtt0113497
3rd rowtt0113228
4th rowtt0114885
5th rowtt0113041
ValueCountFrequency (%)
tt1180333 3
 
< 0.1%
0 3
 
< 0.1%
tt0046468 2
 
< 0.1%
tt1327820 2
 
< 0.1%
tt2818654 2
 
< 0.1%
tt0111613 2
 
< 0.1%
tt1821641 2
 
< 0.1%
tt0127834 2
 
< 0.1%
tt0295682 2
 
< 0.1%
tt0080000 2
 
< 0.1%
Other values (45407) 45427
> 99.9%
2025-03-05T11:18:51.999197image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 409017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 90892
22.2%
0 69913
17.1%
1 37232
9.1%
2 31234
 
7.6%
4 28498
 
7.0%
3 28135
 
6.9%
8 25445
 
6.2%
6 25442
 
6.2%
5 24253
 
5.9%
7 24221
 
5.9%
Distinct92
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size355.3 KiB
2025-03-05T11:18:52.065320image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.000154
Min length2

Characters and Unicode

Total characters90917
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32269
71.0%
fr 2438
 
5.4%
it 1529
 
3.4%
ja 1350
 
3.0%
de 1080
 
2.4%
es 994
 
2.2%
ru 826
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 409
 
0.9%
Other values (82) 3608
 
7.9%
2025-03-05T11:18:52.183058image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 34598
38.1%
n 32978
36.3%
r 3636
 
4.0%
f 2839
 
3.1%
i 2391
 
2.6%
t 2252
 
2.5%
a 1841
 
2.0%
s 1654
 
1.8%
j 1351
 
1.5%
d 1325
 
1.5%
Other values (23) 6052
 
6.7%
Distinct43373
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:52.339607image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length109
Median length84
Mean length16.323494
Min length1

Characters and Unicode

Total characters742164
Distinct characters2946
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41712 ?
Unique (%)91.7%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 10261
 
7.8%
of 3309
 
2.5%
a 1674
 
1.3%
in 1275
 
1.0%
and 1072
 
0.8%
la 1007
 
0.8%
863
 
0.7%
to 806
 
0.6%
de 702
 
0.5%
man 509
 
0.4%
Other values (35324) 110301
83.7%
2025-03-05T11:18:52.585967image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 742164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
86293
 
11.6%
e 70665
 
9.5%
a 49100
 
6.6%
o 42066
 
5.7%
i 39494
 
5.3%
n 39149
 
5.3%
r 37728
 
5.1%
t 33530
 
4.5%
s 28615
 
3.9%
l 25557
 
3.4%
Other values (2936) 289967
39.1%

popularity
Unsupported

Rejected  Unsupported 

Missing5
Missing (%)< 0.1%
Memory size355.3 KiB
Distinct17336
Distinct (%)38.2%
Missing87
Missing (%)0.2%
Memory size355.3 KiB
2025-03-05T11:18:52.730148image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9994491
Min length1

Characters and Unicode

Total characters453765
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8573 ?
Unique (%)18.9%

Sample

1st row1995-10-30
2nd row1995-12-15
3rd row1995-12-22
4th row1995-12-22
5th row1995-02-10
ValueCountFrequency (%)
2008-01-01 136
 
0.3%
2009-01-01 121
 
0.3%
2007-01-01 118
 
0.3%
2005-01-01 111
 
0.2%
2006-01-01 101
 
0.2%
2002-01-01 96
 
0.2%
2004-01-01 90
 
0.2%
2001-01-01 84
 
0.2%
2003-01-01 76
 
0.2%
1997-01-01 69
 
0.2%
Other values (17326) 44377
97.8%
2025-03-05T11:18:52.918427image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 453765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 453765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 453765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 97600
21.5%
- 90752
20.0%
1 84056
18.5%
2 52806
11.6%
9 39773
8.8%
3 15435
 
3.4%
8 15279
 
3.4%
6 15021
 
3.3%
5 14836
 
3.3%
7 14289
 
3.1%

revenue
Real number (ℝ)

High correlation  Zeros 

Distinct6863
Distinct (%)15.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11209349
Minimum0
Maximum2.7879651 × 109
Zeros38052
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:52.977023image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile47808918
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64332247
Coefficient of variation (CV)5.7391602
Kurtosis237.51059
Mean11209349
Median Absolute Deviation (MAD)0
Skewness12.265983
Sum5.0957698 × 1011
Variance4.138638 × 1015
MonotonicityNot monotonic
2025-03-05T11:18:53.044761image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38052
83.7%
12000000 20
 
< 0.1%
10000000 19
 
< 0.1%
11000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
500000 13
 
< 0.1%
8000000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7263
 
16.0%
ValueCountFrequency (%)
0 38052
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

Zeros 

Distinct353
Distinct (%)0.8%
Missing263
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean94.128199
Minimum0
Maximum1256
Zeros1558
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:53.115374image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.40781
Coefficient of variation (CV)0.40803724
Kurtosis93.217158
Mean94.128199
Median Absolute Deviation (MAD)11
Skewness4.4659579
Sum4254877
Variance1475.1599
MonotonicityNot monotonic
2025-03-05T11:18:53.186027image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2556
 
5.6%
0 1558
 
3.4%
100 1470
 
3.2%
95 1412
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1080
 
2.4%
94 1062
 
2.3%
91 1057
 
2.3%
88 1032
 
2.3%
Other values (343) 31658
69.6%
ValueCountFrequency (%)
0 1558
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 51
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%
Distinct1931
Distinct (%)4.2%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
2025-03-05T11:18:53.362296image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length765
Median length40
Mean length46.928289
Min length2

Characters and Unicode

Total characters2133360
Distinct characters184
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1366 ?
Unique (%)3.0%

Sample

1st row[{'iso_639_1': 'en', 'name': 'English'}]
2nd row[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}]
3rd row[{'iso_639_1': 'en', 'name': 'English'}]
4th row[{'iso_639_1': 'en', 'name': 'English'}]
5th row[{'iso_639_1': 'en', 'name': 'English'}]
ValueCountFrequency (%)
iso_639_1 53300
24.4%
name 53300
24.4%
english 28745
13.2%
en 28745
13.2%
4809
 
2.2%
fr 4196
 
1.9%
français 4196
 
1.9%
deutsch 2625
 
1.2%
de 2625
 
1.2%
es 2413
 
1.1%
Other values (203) 33488
15.3%
2025-03-05T11:18:53.614617image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2133360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2133360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2133360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 426400
20.0%
172982
 
8.1%
n 120605
 
5.7%
_ 106600
 
5.0%
: 106600
 
5.0%
s 99222
 
4.7%
i 94120
 
4.4%
e 92748
 
4.3%
a 75235
 
3.5%
, 64969
 
3.0%
Other values (174) 773879
36.3%

status
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing87
Missing (%)0.2%
Memory size355.3 KiB
Released
45014 
Rumored
 
230
Post Production
 
98
In Production
 
20
Planned
 
15

Length

Max length15
Median length8
Mean length8.0119218
Min length7

Characters and Unicode

Total characters363573
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 45014
99.0%
Rumored 230
 
0.5%
Post Production 98
 
0.2%
In Production 20
 
< 0.1%
Planned 15
 
< 0.1%
Canceled 2
 
< 0.1%
(Missing) 87
 
0.2%

Length

2025-03-05T11:18:53.667737image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T11:18:53.713444image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
released 45014
98.9%
rumored 230
 
0.5%
production 118
 
0.3%
post 98
 
0.2%
in 20
 
< 0.1%
planned 15
 
< 0.1%
canceled 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 363573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 135291
37.2%
d 45379
 
12.5%
R 45244
 
12.4%
s 45112
 
12.4%
l 45031
 
12.4%
a 45031
 
12.4%
o 564
 
0.2%
r 348
 
0.1%
u 348
 
0.1%
P 231
 
0.1%
Other values (8) 994
 
0.3%

title
Text

Distinct42277
Distinct (%)93.0%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
2025-03-05T11:18:53.856119image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.708535
Min length1

Characters and Unicode

Total characters759570
Distinct characters287
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39947 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14571
 
10.7%
of 4938
 
3.6%
a 2244
 
1.6%
in 1697
 
1.2%
and 1634
 
1.2%
to 1055
 
0.8%
763
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 602
 
0.4%
Other values (24431) 107634
78.9%
2025-03-05T11:18:54.083634image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 759570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
91029
 
12.0%
e 76408
 
10.1%
a 49056
 
6.5%
o 45765
 
6.0%
n 40931
 
5.4%
r 40096
 
5.3%
i 39859
 
5.2%
t 36792
 
4.8%
s 29591
 
3.9%
h 28564
 
3.8%
Other values (277) 281479
37.1%

video
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size355.3 KiB
False
45367 
True
 
93
(Missing)
 
6
ValueCountFrequency (%)
False 45367
99.8%
True 93
 
0.2%
(Missing) 6
 
< 0.1%
2025-03-05T11:18:54.117157image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

vote_average
Real number (ℝ)

Zeros 

Distinct92
Distinct (%)0.2%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.6182072
Minimum0
Maximum10
Zeros2998
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:54.166734image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.924216
Coefficient of variation (CV)0.34249644
Kurtosis2.5004022
Mean5.6182072
Median Absolute Deviation (MAD)0.9
Skewness-1.5189901
Sum255403.7
Variance3.7026072
MonotonicityNot monotonic
2025-03-05T11:18:54.380422image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2998
 
6.6%
6 2468
 
5.4%
5 2001
 
4.4%
7 1886
 
4.1%
6.5 1722
 
3.8%
6.3 1603
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1350
 
3.0%
6.7 1342
 
3.0%
Other values (82) 27340
60.1%
ValueCountFrequency (%)
0 2998
6.6%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 105
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 190
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 3
 
< 0.1%
9 159
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

High correlation  Zeros 

Distinct1820
Distinct (%)4.0%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean109.89734
Minimum0
Maximum14075
Zeros2899
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size355.3 KiB
2025-03-05T11:18:54.444547image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.31037
Coefficient of variation (CV)4.4706303
Kurtosis151.2028
Mean109.89734
Median Absolute Deviation (MAD)8
Skewness10.450232
Sum4995933
Variance241385.88
MonotonicityNot monotonic
2025-03-05T11:18:54.512643image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3264
 
7.2%
2 3132
 
6.9%
0 2899
 
6.4%
3 2787
 
6.1%
4 2480
 
5.5%
5 2097
 
4.6%
6 1747
 
3.8%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22931
50.4%
ValueCountFrequency (%)
0 2899
6.4%
1 3264
7.2%
2 3132
6.9%
3 2787
6.1%
4 2480
5.5%
5 2097
4.6%
6 1747
3.8%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

Interactions

2025-03-05T11:18:49.620882image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:48.952348image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.177558image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.400386image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.680024image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.009954image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.234615image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.454968image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.737467image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.065023image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.287682image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.508033image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.795046image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.119482image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.343297image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-05T11:18:49.559590image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-05T11:18:54.564217image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
adultrevenueruntimestatusvideovote_averagevote_count
adult1.0000.0000.0000.0740.0000.0190.000
revenue0.0001.0000.2540.0000.0000.1270.513
runtime0.0000.2541.0000.0000.0590.1940.291
status0.0740.0000.0001.0000.0000.0190.000
video0.0000.0000.0590.0001.0000.0470.000
vote_average0.0190.1270.1940.0190.0471.0000.320
vote_count0.0000.5130.2910.0000.0000.3201.000

Missing values

2025-03-05T11:18:50.000282image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-05T11:18:50.120486image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-05T11:18:50.355693image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

adultbudgetgenresidimdb_idoriginal_languageoriginal_titlepopularityrelease_daterevenueruntimespoken_languagesstatustitlevideovote_averagevote_count
0False30000000[{'id': 16, 'name': 'Animation'}, {'id': 35, 'name': 'Comedy'}, {'id': 10751, 'name': 'Family'}]862tt0114709enToy Story21.9469431995-10-30373554033.081.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedToy StoryFalse7.75415.0
1False65000000[{'id': 12, 'name': 'Adventure'}, {'id': 14, 'name': 'Fantasy'}, {'id': 10751, 'name': 'Family'}]8844tt0113497enJumanji17.0155391995-12-15262797249.0104.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'fr', 'name': 'Français'}]ReleasedJumanjiFalse6.92413.0
2False0[{'id': 10749, 'name': 'Romance'}, {'id': 35, 'name': 'Comedy'}]15602tt0113228enGrumpier Old Men11.71291995-12-220.0101.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedGrumpier Old MenFalse6.592.0
3False16000000[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]31357tt0114885enWaiting to Exhale3.8594951995-12-2281452156.0127.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedWaiting to ExhaleFalse6.134.0
4False0[{'id': 35, 'name': 'Comedy'}]11862tt0113041enFather of the Bride Part II8.3875191995-02-1076578911.0106.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedFather of the Bride Part IIFalse5.7173.0
5False60000000[{'id': 28, 'name': 'Action'}, {'id': 80, 'name': 'Crime'}, {'id': 18, 'name': 'Drama'}, {'id': 53, 'name': 'Thriller'}]949tt0113277enHeat17.9249271995-12-15187436818.0170.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'es', 'name': 'Español'}]ReleasedHeatFalse7.71886.0
6False58000000[{'id': 35, 'name': 'Comedy'}, {'id': 10749, 'name': 'Romance'}]11860tt0114319enSabrina6.6772771995-12-150.0127.0[{'iso_639_1': 'fr', 'name': 'Français'}, {'iso_639_1': 'en', 'name': 'English'}]ReleasedSabrinaFalse6.2141.0
7False0[{'id': 28, 'name': 'Action'}, {'id': 12, 'name': 'Adventure'}, {'id': 18, 'name': 'Drama'}, {'id': 10751, 'name': 'Family'}]45325tt0112302enTom and Huck2.5611611995-12-220.097.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'de', 'name': 'Deutsch'}]ReleasedTom and HuckFalse5.445.0
8False35000000[{'id': 28, 'name': 'Action'}, {'id': 12, 'name': 'Adventure'}, {'id': 53, 'name': 'Thriller'}]9091tt0114576enSudden Death5.231581995-12-2264350171.0106.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedSudden DeathFalse5.5174.0
9False58000000[{'id': 12, 'name': 'Adventure'}, {'id': 28, 'name': 'Action'}, {'id': 53, 'name': 'Thriller'}]710tt0113189enGoldenEye14.6860361995-11-16352194034.0130.0[{'iso_639_1': 'en', 'name': 'English'}, {'iso_639_1': 'ru', 'name': 'Pусский'}, {'iso_639_1': 'es', 'name': 'Español'}]ReleasedGoldenEyeFalse6.61194.0
adultbudgetgenresidimdb_idoriginal_languageoriginal_titlepopularityrelease_daterevenueruntimespoken_languagesstatustitlevideovote_averagevote_count
45456False0[{'id': 27, 'name': 'Horror'}, {'id': 9648, 'name': 'Mystery'}, {'id': 53, 'name': 'Thriller'}]84419tt0038621enHouse of Horrors0.2228141946-03-290.065.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedHouse of HorrorsFalse6.38.0
45457False0[{'id': 9648, 'name': 'Mystery'}, {'id': 27, 'name': 'Horror'}]390959tt0265736enShadow of the Blair Witch0.0760612000-10-220.045.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedShadow of the Blair WitchFalse7.02.0
45458False0[{'id': 27, 'name': 'Horror'}]289923tt0252966enThe Burkittsville 70.386452000-10-030.030.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedThe Burkittsville 7False7.01.0
45459False0[{'id': 878, 'name': 'Science Fiction'}]222848tt0112613enCaged Heat 30000.6615581995-01-010.085.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedCaged Heat 3000False3.51.0
45460False0[{'id': 18, 'name': 'Drama'}, {'id': 28, 'name': 'Action'}, {'id': 10749, 'name': 'Romance'}]30840tt0102797enRobin Hood5.6837531991-05-130.0104.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedRobin HoodFalse5.726.0
45461False0[{'id': 18, 'name': 'Drama'}, {'id': 10751, 'name': 'Family'}]439050tt6209470faرگ خواب0.072051NaN0.090.0[{'iso_639_1': 'fa', 'name': 'فارسی'}]ReleasedSubdueFalse4.01.0
45462False0[{'id': 18, 'name': 'Drama'}]111109tt2028550tlSiglo ng Pagluluwal0.1782412011-11-170.0360.0[{'iso_639_1': 'tl', 'name': ''}]ReleasedCentury of BirthingFalse9.03.0
45463False0[{'id': 28, 'name': 'Action'}, {'id': 18, 'name': 'Drama'}, {'id': 53, 'name': 'Thriller'}]67758tt0303758enBetrayal0.9030072003-08-010.090.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedBetrayalFalse3.86.0
45464False0[]227506tt0008536enSatana likuyushchiy0.0035031917-10-210.087.0[]ReleasedSatan TriumphantFalse0.00.0
45465False0[]461257tt6980792enQueerama0.1630152017-06-090.075.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedQueeramaFalse0.00.0

Duplicate rows

Most frequently occurring

adultbudgetgenresidimdb_idoriginal_languageoriginal_titlerelease_daterevenueruntimespoken_languagesstatustitlevideovote_averagevote_count# duplicates
15False0[{'id': 53, 'name': 'Thriller'}, {'id': 9648, 'name': 'Mystery'}]141971tt1180333fiBlackout2008-12-260.0108.0[{'iso_639_1': 'fi', 'name': 'suomi'}]ReleasedBlackoutFalse6.73.03
0False0[{'id': 12, 'name': 'Adventure'}, {'id': 14, 'name': 'Fantasy'}, {'id': 16, 'name': 'Animation'}, {'id': 878, 'name': 'Science Fiction'}, {'id': 10751, 'name': 'Family'}]12600tt0287635ja劇場版ポケットモンスター セレビィ 時を越えた遭遇(であい)2001-07-0628023563.075.0[{'iso_639_1': 'ja', 'name': '日本語'}]ReleasedPokémon 4Ever: Celebi - Voice of the ForestFalse5.782.02
1False0[{'id': 12, 'name': 'Adventure'}, {'id': 16, 'name': 'Animation'}, {'id': 18, 'name': 'Drama'}, {'id': 28, 'name': 'Action'}, {'id': 10769, 'name': 'Foreign'}]23305tt0295682enThe Warrior2001-09-230.086.0[{'iso_639_1': 'hi', 'name': 'हिन्दी'}]ReleasedThe WarriorFalse6.315.02
2False0[{'id': 14, 'name': 'Fantasy'}, {'id': 18, 'name': 'Drama'}, {'id': 878, 'name': 'Science Fiction'}]119916tt0080000enThe Tempest1980-02-270.0123.0[]ReleasedThe TempestFalse0.00.02
3False0[{'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]105045tt0111613deDas Versprechen1995-02-160.0115.0[{'iso_639_1': 'de', 'name': 'Deutsch'}]ReleasedThe PromiseFalse5.01.02
4False0[{'id': 18, 'name': 'Drama'}, {'id': 10769, 'name': 'Foreign'}]42495tt0067306enKing Lear1971-02-040.0137.0[{'iso_639_1': 'en', 'name': 'English'}]RumoredKing LearFalse8.03.02
5False0[{'id': 18, 'name': 'Drama'}, {'id': 35, 'name': 'Comedy'}]168538tt0084387enNana1983-06-130.092.0[]ReleasedNana, the True Key of PleasureFalse4.73.02
6False0[{'id': 18, 'name': 'Drama'}, {'id': 878, 'name': 'Science Fiction'}, {'id': 16, 'name': 'Animation'}]152795tt1821641enThe Congress2013-05-16455815.0122.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedThe CongressFalse6.4165.02
7False0[{'id': 18, 'name': 'Drama'}]109962tt0082992enRich and Famous1981-09-230.0115.0[{'iso_639_1': 'en', 'name': 'English'}]ReleasedRich and FamousFalse4.97.02
8False0[{'id': 18, 'name': 'Drama'}]132641tt0046468jaTsuma1953-04-290.089.0[{'iso_639_1': 'ja', 'name': '日本語'}]ReleasedWifeFalse0.00.02